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3 дня назад

HFT Researcher (ML)

Тип работы
fulltime
Английский
b2
Страна
UAE/Netherlands/Canada
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
HFT Researcher (ML): Building agent-driven research systems for short-horizon systematic trading with an accent on market microstructure, modeling, and scalable experimentation. Focus on formalizing research lifecycles, developing realistic backtesting and simulation frameworks, and integrating continuously learning systems into live trading pipelines.

Location: Amsterdam, Montreal, or Dubai

Company

hirify.global develops systematic trading research and production systems focused on high-frequency trading.

What you will do

  • Formalize short-horizon trading research through hypothesis spaces, validation logic, and end-to-end evaluation lifecycles.
  • Design and build autonomous agents that generate, test, and optimize trading strategies.
  • Convert market microstructure expertise into executable features, signals, and constraints.
  • Develop backtesting and simulation frameworks for large-scale experimentation under realistic execution conditions.
  • Improve research throughput, exploration–exploitation balance, and statistical robustness.
  • Partner with engineering to integrate agent-based systems into production trading pipelines.

Requirements

  • Expertise in short-horizon systematic or high-frequency trading research.
  • Strong understanding of market microstructure, quantitative modeling, and trading signals.
  • Experience designing research methodologies, validation processes, and statistical evaluations.
  • Ability to build or adapt backtesting and simulation frameworks.
  • Experience applying machine learning or autonomous-agent approaches to real-world research systems.
  • Availability to work from Amsterdam, Montreal, or Dubai.

Culture & Benefits

  • Work on frontier real-world machine learning applications in systematic trading.
  • Freedom to define research problems, test ideas, and move successful approaches into live systems.
  • Close collaboration between research and engineering.
  • Opportunity to improve continuously learning trading infrastructure at scale.

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